Overview

Dataset statistics

Number of variables17
Number of observations278
Missing cells27
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.0 KiB
Average record size in memory136.5 B

Variable types

DateTime1
TimeSeries16

Timeseries statistics

Number of series16
Time series length278
Starting point0
Ending point277
Period1
2024-02-06T10:06:35.967138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:36.272113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Alerts

CNY is highly overall correlated with EUR and 10 other fieldsHigh correlation
Coffee is highly overall correlated with Iron Ore and 10 other fieldsHigh correlation
EUR is highly overall correlated with CNY and 8 other fieldsHigh correlation
Iron Ore is highly overall correlated with Coffee and 10 other fieldsHigh correlation
Meat index is highly overall correlated with CNY and 11 other fieldsHigh correlation
Soybeans is highly overall correlated with Coffee and 10 other fieldsHigh correlation
Sugar is highly overall correlated with Coffee and 10 other fieldsHigh correlation
USD is highly overall correlated with CNY and 8 other fieldsHigh correlation
bud_group_personal_spent_value is highly overall correlated with CNY and 14 other fieldsHigh correlation
bud_type_mandatory_spent_value is highly overall correlated with CNY and 14 other fieldsHigh correlation
eco_GDP_R$_12_months is highly overall correlated with CNY and 14 other fieldsHigh correlation
eco_net_debt_R$ is highly overall correlated with CNY and 14 other fieldsHigh correlation
eco_net_debt_R$_federal_govt is highly overall correlated with CNY and 14 other fieldsHigh correlation
eco_total_revenue is highly overall correlated with CNY and 14 other fieldsHigh correlation
exp_DIC_y is highly overall correlated with CNY and 11 other fieldsHigh correlation
exp_trade_balance_y is highly overall correlated with CNY and 8 other fieldsHigh correlation
eco_net_debt_R$ has 3 (1.1%) missing valuesMissing
eco_net_debt_R$_federal_govt has 3 (1.1%) missing valuesMissing
eco_total_revenue has 3 (1.1%) missing valuesMissing
eco_net_debt_R$ is non stationaryNon stationary
eco_net_debt_R$_federal_govt is non stationaryNon stationary
eco_GDP_R$_12_months is non stationaryNon stationary
Coffee is non stationaryNon stationary
Iron Ore is non stationaryNon stationary
Meat index is non stationaryNon stationary
Soybeans is non stationaryNon stationary
Sugar is non stationaryNon stationary
bud_group_personal_spent_value is non stationaryNon stationary
bud_type_mandatory_spent_value is non stationaryNon stationary
exp_DIC_y is non stationaryNon stationary
exp_trade_balance_y is non stationaryNon stationary
CNY is non stationaryNon stationary
EUR is non stationaryNon stationary
USD is non stationaryNon stationary
eco_total_revenue is non stationaryNon stationary
Iron Ore is seasonalSeasonal
date has unique valuesUnique

Reproduction

Analysis started2024-02-06 09:05:56.682357
Analysis finished2024-02-06 09:06:35.863961
Duration39.18 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

date
Date

UNIQUE 

Distinct278
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2001-01-01 00:00:00
Maximum2024-02-01 00:00:00
2024-02-06T10:06:36.574186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:36.793704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

eco_net_debt_R$
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct275
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean2316953.8
Minimum541333.74
Maximum6333747.9
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:36.987440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum541333.74
5-th percentile665559.39
Q11061685.4
median1567887.3
Q33550458.9
95-th percentile5552878.9
Maximum6333747.9
Range5792414.2
Interquartile range (IQR)2488773.5

Descriptive statistics

Standard deviation1635211.4
Coefficient of variation (CV)0.70575916
Kurtosis-0.44048864
Mean2316953.8
Median Absolute Deviation (MAD)625662.63
Skewness0.97119847
Sum6.3716229 × 108
Variance2.6739162 × 1012
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2024-02-06T10:06:37.174917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:37.706457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:37.802239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2423128.13 1
 
0.4%
2805851.53 1
 
0.4%
2742366.38 1
 
0.4%
2672668.3 1
 
0.4%
2627112.1 1
 
0.4%
2493775.18 1
 
0.4%
2458650.72 1
 
0.4%
2360392.03 1
 
0.4%
2033575.55 1
 
0.4%
2286788.39 1
 
0.4%
Other values (265) 265
95.3%
(Missing) 3
 
1.1%
ValueCountFrequency (%)
541333.74 1
0.4%
550253.48 1
0.4%
561959.2 1
0.4%
565464.63 1
0.4%
581727.09 1
0.4%
586060.21 1
0.4%
606367.82 1
0.4%
622953.86 1
0.4%
635685.65 1
0.4%
638367.6 1
0.4%
ValueCountFrequency (%)
6333747.91 1
0.4%
6253007.52 1
0.4%
6211220.8 1
0.4%
6163293.13 1
0.4%
6067062.7 1
0.4%
5992871.68 1
0.4%
5922818.04 1
0.4%
5817539.24 1
0.4%
5794341.14 1
0.4%
5719397.77 1
0.4%
2024-02-06T10:06:37.357877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

eco_net_debt_R$_federal_govt
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct275
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean1655032.4
Minimum337611.99
Maximum5473823.1
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:37.969041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum337611.99
5-th percentile425874.02
Q1708802.82
median1017171.8
Q32549900.9
95-th percentile4619428.7
Maximum5473823.1
Range5136211.1
Interquartile range (IQR)1841098.1

Descriptive statistics

Standard deviation1338846.9
Coefficient of variation (CV)0.8089551
Kurtosis0.39020203
Mean1655032.4
Median Absolute Deviation (MAD)423893.09
Skewness1.245133
Sum4.5513392 × 108
Variance1.7925111 × 1012
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2024-02-06T10:06:38.155094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:38.653124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:38.956860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1533881.44 1
 
0.4%
1897749.65 1
 
0.4%
1836057.82 1
 
0.4%
1820041.3 1
 
0.4%
1746541.89 1
 
0.4%
1588570.55 1
 
0.4%
1575573.76 1
 
0.4%
1399516.92 1
 
0.4%
1238357.26 1
 
0.4%
1292031.16 1
 
0.4%
Other values (265) 265
95.3%
(Missing) 3
 
1.1%
ValueCountFrequency (%)
337611.99 1
0.4%
346552.5 1
0.4%
355037.5 1
0.4%
360694.52 1
0.4%
374879.99 1
0.4%
378378.89 1
0.4%
395250.93 1
0.4%
405832.79 1
0.4%
408431.08 1
0.4%
411771.96 1
0.4%
ValueCountFrequency (%)
5473823.08 1
0.4%
5402090.79 1
0.4%
5375619.97 1
0.4%
5315415.06 1
0.4%
5256528.02 1
0.4%
5169640.12 1
0.4%
5006344.84 1
0.4%
4915045.88 1
0.4%
4877969.97 1
0.4%
4806136.53 1
0.4%
2024-02-06T10:06:38.319510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

eco_GDP_R$_12_months
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct276
Distinct (%)100.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean4813004.3
Minimum1209046.1
Maximum10867757
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:39.122395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1209046.1
5-th percentile1343047.1
Q12356119.1
median4606582.8
Q36694390.5
95-th percentile9921932.7
Maximum10867757
Range9658710.8
Interquartile range (IQR)4338271.4

Descriptive statistics

Standard deviation2655408.3
Coefficient of variation (CV)0.55171533
Kurtosis-0.78427833
Mean4813004.3
Median Absolute Deviation (MAD)2187904.1
Skewness0.4403895
Sum1.3283892 × 109
Variance7.051193 × 1012
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.998114555
2024-02-06T10:06:39.310913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:39.918294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:40.011040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
6063135.6 1
 
0.4%
6205827.1 1
 
0.4%
6187449.8 1
 
0.4%
6162651.9 1
 
0.4%
6133275.9 1
 
0.4%
6118507.6 1
 
0.4%
6087360.6 1
 
0.4%
6039427.2 1
 
0.4%
5927545.4 1
 
0.4%
6026845.6 1
 
0.4%
Other values (266) 266
95.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
1209046.1 1
0.4%
1218911 1
0.4%
1234635 1
0.4%
1250830.7 1
0.4%
1263306 1
0.4%
1265570 1
0.4%
1272918.6 1
0.4%
1280805.4 1
0.4%
1289198.6 1
0.4%
1298386.5 1
0.4%
ValueCountFrequency (%)
10867756.9 1
0.4%
10801619.3 1
0.4%
10728151.8 1
0.4%
10666257.2 1
0.4%
10618789 1
0.4%
10568766.9 1
0.4%
10526477.7 1
0.4%
10476176.7 1
0.4%
10414652.1 1
0.4%
10342854.3 1
0.4%
2024-02-06T10:06:39.553242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

Coffee
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct276
Distinct (%)100.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean91.62979
Minimum33.181211
Maximum183.39671
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:40.180562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum33.181211
5-th percentile37.644156
Q172.800572
median89.413025
Q3109.94264
95-th percentile162.30549
Maximum183.39671
Range150.2155
Interquartile range (IQR)37.142072

Descriptive statistics

Standard deviation35.103615
Coefficient of variation (CV)0.38310265
Kurtosis-0.18050115
Mean91.62979
Median Absolute Deviation (MAD)18.070015
Skewness0.42620432
Sum25289.822
Variance1232.2638
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2882213169
2024-02-06T10:06:40.373960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:40.905481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:40.988803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
94.12697354 1
 
0.4%
109.2256194 1
 
0.4%
107.6022162 1
 
0.4%
102.2591064 1
 
0.4%
104.8363193 1
 
0.4%
100.9840626 1
 
0.4%
94.72324215 1
 
0.4%
96.21052189 1
 
0.4%
94.26668466 1
 
0.4%
90.14719033 1
 
0.4%
Other values (266) 266
95.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
33.18121093 1
0.4%
34.40419394 1
0.4%
34.47106725 1
0.4%
34.53689171 1
0.4%
35.54873591 1
0.4%
35.56335781 1
0.4%
35.73944425 1
0.4%
35.91780564 1
0.4%
35.97495675 1
0.4%
36.08154634 1
0.4%
ValueCountFrequency (%)
183.3967108 1
0.4%
178.2638053 1
0.4%
177.652549 1
0.4%
176.018388 1
0.4%
170.7968299 1
0.4%
167.8350432 1
0.4%
167.7737306 1
0.4%
167.0920788 1
0.4%
165.4549889 1
0.4%
165.0637573 1
0.4%
2024-02-06T10:06:40.571722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

Iron Ore
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct189
Distinct (%)68.5%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean135.72877
Minimum21.651086
Maximum368.50523
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:41.155675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum21.651086
5-th percentile22.180411
Q157.115839
median122.42527
Q3205.13881
95-th percentile288.1126
Maximum368.50523
Range346.85414
Interquartile range (IQR)148.02297

Descriptive statistics

Standard deviation86.046034
Coefficient of variation (CV)0.63395575
Kurtosis-0.77791813
Mean135.72877
Median Absolute Deviation (MAD)72.164736
Skewness0.38577053
Sum37461.141
Variance7403.92
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.495974084
2024-02-06T10:06:41.362623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:41.887213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:41.983931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
22.18041099 12
 
4.3%
21.65108632 12
 
4.3%
23.59763509 12
 
4.3%
27.98590732 12
 
4.3%
47.99779468 12
 
4.3%
57.11583892 12
 
4.3%
62.54568549 12
 
4.3%
103.8159344 11
 
4.0%
123.8091945 1
 
0.4%
114.4800413 1
 
0.4%
Other values (179) 179
64.4%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
21.65108632 12
4.3%
22.18041099 12
4.3%
23.59763509 12
4.3%
27.98590732 12
4.3%
47.99779468 12
4.3%
57.11583892 12
4.3%
62.54568549 12
4.3%
69.80701025 1
 
0.4%
72.06458543 1
 
0.4%
79.374307 1
 
0.4%
ValueCountFrequency (%)
368.5052278 1
0.4%
365.9868711 1
0.4%
346.3783526 1
0.4%
319.6096481 1
0.4%
306.7180313 1
0.4%
306.0881539 1
0.4%
304.3616827 1
0.4%
302.9956836 1
0.4%
302.615377 1
0.4%
302.3895215 1
0.4%
2024-02-06T10:06:41.552086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

Meat index
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct276
Distinct (%)100.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean102.49884
Minimum62.006865
Maximum165.93271
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:42.152503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum62.006865
5-th percentile69.172843
Q184.164519
median102.97732
Q3114.27888
95-th percentile145.0024
Maximum165.93271
Range103.92585
Interquartile range (IQR)30.114357

Descriptive statistics

Standard deviation22.475726
Coefficient of variation (CV)0.21927785
Kurtosis-0.025091961
Mean102.49884
Median Absolute Deviation (MAD)15.195288
Skewness0.54219967
Sum28289.681
Variance505.15828
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.257127254
2024-02-06T10:06:42.351969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:42.857368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:42.940783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100.5686532 1
 
0.4%
94.38700958 1
 
0.4%
99.38668181 1
 
0.4%
103.1002027 1
 
0.4%
108.8547146 1
 
0.4%
109.4729081 1
 
0.4%
106.1814828 1
 
0.4%
99.6748717 1
 
0.4%
114.8734725 1
 
0.4%
98.4100018 1
 
0.4%
Other values (266) 266
95.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
62.00686549 1
0.4%
62.70071092 1
0.4%
64.25407578 1
0.4%
64.57241398 1
0.4%
65.93872914 1
0.4%
66.40308821 1
0.4%
66.50843215 1
0.4%
66.84424609 1
0.4%
66.86704112 1
0.4%
67.67504554 1
0.4%
ValueCountFrequency (%)
165.932715 1
0.4%
165.921585 1
0.4%
165.2829385 1
0.4%
162.8758722 1
0.4%
162.543242 1
0.4%
158.8909804 1
0.4%
153.231688 1
0.4%
151.4055422 1
0.4%
149.2062069 1
0.4%
148.8278397 1
0.4%
2024-02-06T10:06:42.537449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

Soybeans
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct275
Distinct (%)99.6%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean102.15926
Minimum43.729764
Maximum171.74024
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:43.107587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum43.729764
5-th percentile48.616593
Q178.079873
median98.535786
Q3133.60784
95-th percentile153.47634
Maximum171.74024
Range128.01047
Interquartile range (IQR)55.527972

Descriptive statistics

Standard deviation33.869372
Coefficient of variation (CV)0.33153503
Kurtosis-0.96724727
Mean102.15926
Median Absolute Deviation (MAD)31.077455
Skewness0.092702418
Sum28195.955
Variance1147.1344
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.211406095
2024-02-06T10:06:43.307809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:43.863122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:43.959897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
57.40718764 2
 
0.7%
89.20552506 1
 
0.4%
90.27119191 1
 
0.4%
87.97250904 1
 
0.4%
89.1395787 1
 
0.4%
89.10898146 1
 
0.4%
88.26122582 1
 
0.4%
101.5622173 1
 
0.4%
97.545026 1
 
0.4%
107.1146486 1
 
0.4%
Other values (265) 265
95.3%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
43.72976372 1
0.4%
44.12868858 1
0.4%
44.15401715 1
0.4%
44.16668143 1
0.4%
44.2869921 1
0.4%
44.81889192 1
0.4%
45.17982394 1
0.4%
45.31913104 1
0.4%
46.16763789 1
0.4%
46.50957349 1
0.4%
ValueCountFrequency (%)
171.7402359 1
0.4%
171.2672997 1
0.4%
170.4237016 1
0.4%
170.1645162 1
0.4%
170.1437448 1
0.4%
169.6084446 1
0.4%
168.0273765 1
0.4%
160.926947 1
0.4%
159.0728147 1
0.4%
159.033174 1
0.4%
2024-02-06T10:06:43.524699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

Sugar
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct276
Distinct (%)100.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean83.535526
Minimum32.829715
Maximum161.86432
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:44.128435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum32.829715
5-th percentile39.593097
Q161.154622
median79.96087
Q3103.43688
95-th percentile138.70562
Maximum161.86432
Range129.0346
Interquartile range (IQR)42.282259

Descriptive statistics

Standard deviation30.203956
Coefficient of variation (CV)0.36157019
Kurtosis-0.44989
Mean83.535526
Median Absolute Deviation (MAD)22.353397
Skewness0.43882679
Sum23055.805
Variance912.27894
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1802081802
2024-02-06T10:06:44.328906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:44.878406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:44.974149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
84.49101798 1
 
0.4%
124.0038593 1
 
0.4%
116.0327396 1
 
0.4%
109.6329034 1
 
0.4%
107.8418547 1
 
0.4%
105.9130682 1
 
0.4%
92.62755158 1
 
0.4%
86.06779516 1
 
0.4%
67.85186445 1
 
0.4%
75.26326585 1
 
0.4%
Other values (266) 266
95.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
32.82971492 1
0.4%
33.25658032 1
0.4%
35.0114891 1
0.4%
35.75838449 1
0.4%
36.24492899 1
0.4%
36.60505209 1
0.4%
36.63393117 1
0.4%
36.70201554 1
0.4%
37.36242521 1
0.4%
37.85865617 1
0.4%
ValueCountFrequency (%)
161.8643182 1
0.4%
159.980352 1
0.4%
159.8990528 1
0.4%
157.5630484 1
0.4%
152.8481404 1
0.4%
151.6590235 1
0.4%
149.4593334 1
0.4%
147.1682084 1
0.4%
146.355285 1
0.4%
146.2259982 1
0.4%
2024-02-06T10:06:44.521361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

bud_group_personal_spent_value
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct265
Distinct (%)96.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1.9565775 × 1011
Minimum6.2678574 × 1010
Maximum3.6832098 × 1011
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:45.148682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6.2678574 × 1010
5-th percentile6.5508889 × 1010
Q11.0503278 × 1011
median1.8738975 × 1011
Q32.8810099 × 1011
95-th percentile3.3631696 × 1011
Maximum3.6832098 × 1011
Range3.0564241 × 1011
Interquartile range (IQR)1.830682 × 1011

Descriptive statistics

Standard deviation9.4157461 × 1010
Coefficient of variation (CV)0.48123553
Kurtosis-1.3389432
Mean1.9565775 × 1011
Median Absolute Deviation (MAD)9.0370515 × 1010
Skewness0.16651718
Sum5.4001539 × 1013
Variance8.8656275 × 1021
MonotonicityIncreasing
Augmented Dickey-Fuller test p-value0.9945857874
2024-02-06T10:06:45.343125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:45.846615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:46.180236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
6.26785744 × 101012
 
4.3%
6.473698486 × 10101
 
0.4%
2.652749283 × 10111
 
0.4%
2.403932898 × 10111
 
0.4%
2.420699658 × 10111
 
0.4%
2.437466418 × 10111
 
0.4%
2.454233179 × 10111
 
0.4%
2.470999939 × 10111
 
0.4%
2.487766699 × 10111
 
0.4%
2.504533459 × 10111
 
0.4%
Other values (255) 255
91.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
6.26785744 × 101012
4.3%
6.370777963 × 10101
 
0.4%
6.473698486 × 10101
 
0.4%
6.576619009 × 10101
 
0.4%
6.679539532 × 10101
 
0.4%
6.782460055 × 10101
 
0.4%
6.885380578 × 10101
 
0.4%
6.988301101 × 10101
 
0.4%
7.091221624 × 10101
 
0.4%
7.194142147 × 10101
 
0.4%
ValueCountFrequency (%)
3.683209845 × 10111
0.4%
3.65757528 × 10111
0.4%
3.631940715 × 10111
0.4%
3.60630615 × 10111
0.4%
3.580671584 × 10111
0.4%
3.555037019 × 10111
0.4%
3.529402454 × 10111
0.4%
3.503767888 × 10111
0.4%
3.478133323 × 10111
0.4%
3.452498758 × 10111
0.4%
2024-02-06T10:06:45.526732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

bud_type_mandatory_spent_value
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct265
Distinct (%)96.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean9.4939435 × 1011
Minimum1.363717 × 1011
Maximum2.3830425 × 1012
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:46.335010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.363717 × 1011
5-th percentile1.6316194 × 1011
Q14.3560126 × 1011
median8.4314427 × 1011
Q31.3983507 × 1012
95-th percentile2.0450727 × 1012
Maximum2.3830425 × 1012
Range2.2466708 × 1012
Interquartile range (IQR)9.627494 × 1011

Descriptive statistics

Standard deviation5.9064281 × 1011
Coefficient of variation (CV)0.6221259
Kurtosis-0.76282153
Mean9.4939435 × 1011
Median Absolute Deviation (MAD)4.7392245 × 1011
Skewness0.49562009
Sum2.6203284 × 1014
Variance3.4885893 × 1023
MonotonicityIncreasing
Augmented Dickey-Fuller test p-value0.998903644
2024-02-06T10:06:46.516146image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:47.007016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:47.093878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.363716967 × 101112
 
4.3%
1.558555075 × 10111
 
0.4%
1.329283297 × 10121
 
0.4%
1.210204345 × 10121
 
0.4%
1.219582968 × 10121
 
0.4%
1.22896159 × 10121
 
0.4%
1.238340212 × 10121
 
0.4%
1.247718834 × 10121
 
0.4%
1.257097456 × 10121
 
0.4%
1.266476079 × 10121
 
0.4%
Other values (255) 255
91.7%
(Missing) 2
 
0.7%
ValueCountFrequency (%)
1.363716967 × 101112
4.3%
1.461136021 × 10111
 
0.4%
1.558555075 × 10111
 
0.4%
1.655974129 × 10111
 
0.4%
1.753393183 × 10111
 
0.4%
1.850812237 × 10111
 
0.4%
1.948231291 × 10111
 
0.4%
2.045650345 × 10111
 
0.4%
2.143069399 × 10111
 
0.4%
2.240488453 × 10111
 
0.4%
ValueCountFrequency (%)
2.383042464 × 10121
0.4%
2.358983034 × 10121
0.4%
2.334923603 × 10121
0.4%
2.310864173 × 10121
0.4%
2.286804743 × 10121
0.4%
2.262745313 × 10121
0.4%
2.238685882 × 10121
0.4%
2.214626452 × 10121
0.4%
2.190567022 × 10121
0.4%
2.166507592 × 10121
0.4%
2024-02-06T10:06:46.687961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

exp_DIC_y
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct106
Distinct (%)38.3%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean45.44769
Minimum8.3
Maximum85
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:47.265344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile12.08
Q119.8
median55
Q363
95-th percentile80
Maximum85
Range76.7
Interquartile range (IQR)43.2

Descriptive statistics

Standard deviation23.78155
Coefficient of variation (CV)0.52327303
Kurtosis-1.4298568
Mean45.44769
Median Absolute Deviation (MAD)21.13
Skewness-0.073439701
Sum12589.01
Variance565.56213
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6879775049
2024-02-06T10:06:47.464597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:47.979205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:48.079258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
60 35
 
12.6%
80 19
 
6.8%
55 16
 
5.8%
75 11
 
4.0%
16 10
 
3.6%
25 8
 
2.9%
65 6
 
2.2%
30 6
 
2.2%
35 6
 
2.2%
15 6
 
2.2%
Other values (96) 154
55.4%
ValueCountFrequency (%)
8.3 1
 
0.4%
8.65 1
 
0.4%
9 3
1.1%
9.4 1
 
0.4%
10 3
1.1%
10.26 1
 
0.4%
11.5 1
 
0.4%
12 3
1.1%
12.1 1
 
0.4%
12.6 1
 
0.4%
ValueCountFrequency (%)
85 3
 
1.1%
83.2 1
 
0.4%
82.65 1
 
0.4%
82 1
 
0.4%
81.89 1
 
0.4%
81.6 1
 
0.4%
80 19
6.8%
79.5 2
 
0.7%
78.57 1
 
0.4%
78 1
 
0.4%
2024-02-06T10:06:47.660073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

exp_trade_balance_y
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct197
Distinct (%)71.1%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean32.477168
Minimum-2
Maximum81.3
Zeros2
Zeros (%)0.7%
Memory size2.3 KiB
2024-02-06T10:06:48.246150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile1.54
Q115
median29.1
Q351.1
95-th percentile68.1664
Maximum81.3
Range83.3
Interquartile range (IQR)36.1

Descriptive statistics

Standard deviation21.766712
Coefficient of variation (CV)0.67021584
Kurtosis-1.1264242
Mean32.477168
Median Absolute Deviation (MAD)18
Skewness0.18942087
Sum8996.1756
Variance473.78977
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.5600864329
2024-02-06T10:06:48.428546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:48.966455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:49.065206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
40 9
 
3.2%
55 8
 
2.9%
20 6
 
2.2%
24 6
 
2.2%
15 4
 
1.4%
42 4
 
1.4%
10 4
 
1.4%
25 4
 
1.4%
3 4
 
1.4%
16 3
 
1.1%
Other values (187) 225
80.9%
ValueCountFrequency (%)
-2 1
0.4%
-1.5 1
0.4%
-1.32 1
0.4%
-1.25 1
0.4%
-1 2
0.7%
-0.8 1
0.4%
-0.27 1
0.4%
0 2
0.7%
0.5 1
0.4%
1.2 2
0.7%
ValueCountFrequency (%)
81.3 1
0.4%
78.45 1
0.4%
78.4 1
0.4%
75.15 1
0.4%
73 1
0.4%
72.1 1
0.4%
70.85 1
0.4%
70.5 1
0.4%
70.4 1
0.4%
70.37 1
0.4%
2024-02-06T10:06:48.613140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

CNY
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct276
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43847864
Minimum0.22932
Maximum0.8831
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:49.234737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.22932
5-th percentile0.2449251
Q10.28100825
median0.359069
Q30.562375
95-th percentile0.80077
Maximum0.8831
Range0.65378
Interquartile range (IQR)0.28136675

Descriptive statistics

Standard deviation0.18540873
Coefficient of variation (CV)0.42284552
Kurtosis-0.50669573
Mean0.43847864
Median Absolute Deviation (MAD)0.10145
Skewness0.84942461
Sum121.89706
Variance0.034376397
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.7800708466
2024-02-06T10:06:49.436220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:49.956456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:50.065675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.5519 2
 
0.7%
0.488 2
 
0.7%
0.238588 1
 
0.4%
0.4868 1
 
0.4%
0.4852 1
 
0.4%
0.483 1
 
0.4%
0.5464 1
 
0.4%
0.533 1
 
0.4%
0.6073 1
 
0.4%
0.5004 1
 
0.4%
Other values (266) 266
95.7%
ValueCountFrequency (%)
0.22932 1
0.4%
0.232245 1
0.4%
0.233655 1
0.4%
0.234784 1
0.4%
0.236701 1
0.4%
0.238588 1
0.4%
0.239 1
0.4%
0.241477 1
0.4%
0.2415 1
0.4%
0.24153 1
0.4%
ValueCountFrequency (%)
0.8831 1
0.4%
0.881 1
0.4%
0.8787 1
0.4%
0.8696 1
0.4%
0.8625 1
0.4%
0.8543 1
0.4%
0.8523 1
0.4%
0.8439 1
0.4%
0.8423 1
0.4%
0.8348 1
0.4%
2024-02-06T10:06:49.620188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

EUR
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct276
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5896871
Minimum1.8437
Maximum6.7241
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:50.227601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.8437
5-th percentile2.227715
Q12.624305
median3.2679
Q34.26395
95-th percentile6.17556
Maximum6.7241
Range4.8804
Interquartile range (IQR)1.639645

Descriptive statistics

Standard deviation1.2322011
Coefficient of variation (CV)0.34326141
Kurtosis-0.097239254
Mean3.5896871
Median Absolute Deviation (MAD)0.697995
Skewness0.9184363
Sum997.933
Variance1.5183194
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6977548749
2024-02-06T10:06:50.398241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:50.898828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:50.982209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
5.3453 2
 
0.7%
5.5244 2
 
0.7%
1.8437 1
 
0.4%
4.3234 1
 
0.4%
3.6116 1
 
0.4%
3.6183 1
 
0.4%
3.5414 1
 
0.4%
4.0039 1
 
0.4%
3.9484 1
 
0.4%
4.0539 1
 
0.4%
Other values (266) 266
95.7%
ValueCountFrequency (%)
1.8437 1
0.4%
1.89153 1
0.4%
1.90165 1
0.4%
1.94164 1
0.4%
1.9636 1
0.4%
2.00134 1
0.4%
2.02603 1
0.4%
2.04419 1
0.4%
2.06363 1
0.4%
2.08247 1
0.4%
ValueCountFrequency (%)
6.7241 1
0.4%
6.7142 1
0.4%
6.6915 1
0.4%
6.6532 1
0.4%
6.6132 1
0.4%
6.5393 1
0.4%
6.5194 1
0.4%
6.5016 1
0.4%
6.4 1
0.4%
6.3799 1
0.4%
2024-02-06T10:06:50.581904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

USD
Numeric time series

HIGH CORRELATION  NON STATIONARY 

Distinct277
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0581809
Minimum1.5563
Maximum5.7718
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:51.115628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.5563
5-th percentile1.686615
Q12.109375
median2.7189
Q33.846
95-th percentile5.40405
Maximum5.7718
Range4.2155
Interquartile range (IQR)1.736625

Descriptive statistics

Standard deviation1.2044544
Coefficient of variation (CV)0.39384667
Kurtosis-0.64090856
Mean3.0581809
Median Absolute Deviation (MAD)0.74685
Skewness0.7521394
Sum850.1743
Variance1.4507104
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.8219755617
2024-02-06T10:06:51.308684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:51.826518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:51.914284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.744 2
 
0.7%
1.9711 1
 
0.4%
3.5951 1
 
0.4%
3.1811 1
 
0.4%
3.2462 1
 
0.4%
3.2403 1
 
0.4%
3.239 1
 
0.4%
3.2098 1
 
0.4%
3.4508 1
 
0.4%
3.2591 1
 
0.4%
Other values (267) 267
96.0%
ValueCountFrequency (%)
1.5563 1
0.4%
1.5611 1
0.4%
1.5666 1
0.4%
1.5733 1
0.4%
1.5799 1
0.4%
1.5872 1
0.4%
1.5919 1
0.4%
1.6287 1
0.4%
1.6294 1
0.4%
1.6344 1
0.4%
ValueCountFrequency (%)
5.7718 1
0.4%
5.6973 1
0.4%
5.643 1
0.4%
5.6407 1
0.4%
5.6199 1
0.4%
5.5805 1
0.4%
5.5302 1
0.4%
5.476 1
0.4%
5.4759 1
0.4%
5.4713 1
0.4%
2024-02-06T10:06:51.497351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

eco_total_revenue
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct275
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean87511.96
Minimum18150.773
Maximum251744.5
Zeros0
Zeros (%)0.0%
Memory size2.3 KiB
2024-02-06T10:06:52.064584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum18150.773
5-th percentile23854.619
Q146044.881
median83956.971
Q3115539.45
95-th percentile178937.39
Maximum251744.5
Range233593.73
Interquartile range (IQR)69494.574

Descriptive statistics

Standard deviation49144.252
Coefficient of variation (CV)0.56157183
Kurtosis-0.010985355
Mean87511.96
Median Absolute Deviation (MAD)35463.385
Skewness0.70636972
Sum24065789
Variance2.4151575 × 109
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9958826552
2024-02-06T10:06:52.254606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-02-06T10:06:52.752290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2024-02-06T10:06:53.067714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
95778.54245 1
 
0.4%
94769.98509 1
 
0.4%
91808.10351 1
 
0.4%
107415.6916 1
 
0.4%
98128.87617 1
 
0.4%
95218.70539 1
 
0.4%
110895.2211 1
 
0.4%
87850.75267 1
 
0.4%
97090.6966 1
 
0.4%
129384.8506 1
 
0.4%
Other values (265) 265
95.3%
(Missing) 3
 
1.1%
ValueCountFrequency (%)
18150.77305 1
0.4%
20083.74215 1
0.4%
20706.01917 1
0.4%
21186.0382 1
0.4%
21530.52764 1
0.4%
21883.04615 1
0.4%
21889.77466 1
0.4%
22229.49628 1
0.4%
22237.14981 1
0.4%
22633.83209 1
0.4%
ValueCountFrequency (%)
251744.5028 1
0.4%
235321.2927 1
0.4%
215601.823 1
0.4%
210191.0074 1
0.4%
205475.2399 1
0.4%
203888.5405 1
0.4%
202588.2072 1
0.4%
201828.7797 1
0.4%
195085.06 1
0.4%
193902.2095 1
0.4%
2024-02-06T10:06:52.430758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ACF and PACF

Interactions

2024-02-06T10:06:33.080878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:57.450759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:00.007740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:02.377460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:05.738060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:08.457419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:11.143037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:13.292809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:15.579997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:17.774883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:20.018810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:22.350147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:24.664207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:26.772638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:28.929562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:31.095626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:33.217531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:57.709068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:00.139387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:02.543568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:05.893013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:08.630389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:11.296056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:13.449977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:15.696792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:17.919691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:20.143475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:22.484189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:24.809722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:26.904081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:29.049270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:31.217502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:33.331161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:57.996082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:00.248054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:02.691172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:06.062179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:08.777201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:11.437894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:13.598555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:15.832793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:18.049832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:20.270756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:22.632266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:24.995215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:27.032808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:29.167216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:31.350001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:33.447883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:58.129506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:00.402397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:02.939193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:06.276411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:08.920362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:11.571508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:13.755425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:15.966886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:18.197248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:20.384972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:22.811672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:25.121905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:27.164395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:29.298305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:31.475747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:33.601182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:58.262673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:00.531600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:03.384518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:06.446954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:09.093385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:11.705452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:13.894081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:16.080420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:18.333147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:20.522849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:22.954158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:25.256743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:27.320695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:29.431976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:31.602975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:33.714867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:58.404796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:00.683006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:03.637672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:06.614505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:09.227995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:11.855591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:14.037668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:16.213942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:18.492330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:20.920118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:23.105321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:25.389853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:27.470477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:29.556669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:31.723817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:33.856205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:58.532460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:00.812701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:03.804291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:06.757531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:09.369103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:11.990273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:14.190259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:16.357783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:18.633487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:21.058732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:23.274077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:25.523654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:27.609551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:29.672579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:31.856457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:33.983978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:58.669227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:00.951378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:04.076075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:06.902143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:09.511568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:12.122710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:14.340861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:16.484797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:18.783163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:21.190026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:23.444253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:25.642965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:27.750192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:29.798253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:32.001753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:34.111472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:58.802986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:01.106147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:04.257590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:07.041770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:09.644370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:12.254519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:14.484476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:16.615453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:18.933758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:21.319103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:23.571868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:25.773675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:27.885078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:29.920949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:32.124655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:34.246014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:58.995444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:01.267289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:04.437109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:07.272155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:09.805116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:12.394906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:14.636070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:16.756104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:19.078372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:21.455734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:23.721619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:25.907071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:28.029473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:30.056510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:32.255175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:34.370373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:59.131080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:01.414895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:04.604662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:07.535450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:09.939773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:12.525559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:14.778688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:16.910134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:19.210321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:21.574134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:23.856678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:26.023755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:28.142685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:30.168863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:32.386301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:34.517977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:59.285666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:01.579454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:04.782186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:07.743697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:10.375998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:12.674035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:14.923231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:17.067288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:19.355931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:21.717450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:23.999742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:26.160171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:28.293129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:30.299974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:32.527948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:34.661592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:59.453219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:01.746009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:05.029524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:07.891876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:10.525851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:12.790612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:15.047816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:17.196536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:19.488576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:21.840233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:24.142361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:26.280458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:28.428459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:30.397554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:32.642264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:34.797300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:59.618777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:01.910568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:05.262370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:08.056926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:10.692481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:12.930017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:15.220405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:17.356042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:19.634526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:21.981825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:24.293132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:26.414910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:28.559774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:30.528312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:32.771748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:34.921281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:59.749426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:02.060167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:05.425632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:08.190985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:10.864963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:13.057364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:15.349209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:17.512337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:19.764276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:22.105168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:24.418795image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:26.530833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:28.681817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:30.648748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:32.866794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:35.030691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:05:59.872102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:02.209767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:05.574214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:08.310694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:11.016922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:13.173565image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:15.446520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:17.641994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:19.885951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:22.223908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:24.537740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:26.641929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:28.796860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:30.745323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-06T10:06:32.980828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-06T10:06:53.216721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CNYCoffeeEURIron OreMeat indexSoybeansSugarUSDbud_group_personal_spent_valuebud_type_mandatory_spent_valueeco_GDP_R$_12_monthseco_net_debt_R$eco_net_debt_R$_federal_govteco_total_revenueexp_DIC_yexp_trade_balance_y
CNY1.0000.2490.9320.2290.5290.2240.1150.9550.7560.7560.7570.7490.7320.7130.5800.531
Coffee0.2491.0000.1670.8240.7920.8240.8710.0550.6950.6950.6950.6980.7110.6950.5550.262
EUR0.9320.1671.0000.1500.4340.1650.0270.9310.7040.7040.7040.6990.6860.6560.4600.673
Iron Ore0.2290.8240.1501.0000.7560.8120.8210.0120.6870.6870.6860.6860.6900.6900.5640.236
Meat index0.5290.7920.4340.7561.0000.7450.6690.3370.8280.8280.8280.8290.8340.8260.7370.368
Soybeans0.2240.8240.1650.8120.7451.0000.771-0.0010.6320.6320.6320.6320.6320.6510.5220.221
Sugar0.1150.8710.0270.8210.6690.7711.000-0.0800.6000.6000.6000.5990.6070.6160.5110.235
USD0.9550.0550.9310.0120.337-0.001-0.0801.0000.6130.6130.6130.6060.5920.5590.4220.572
bud_group_personal_spent_value0.7560.6950.7040.6870.8280.6320.6000.6131.0001.0001.0000.9990.9950.9710.8530.639
bud_type_mandatory_spent_value0.7560.6950.7040.6870.8280.6320.6000.6131.0001.0001.0000.9990.9950.9710.8530.639
eco_GDP_R$_12_months0.7570.6950.7040.6860.8280.6320.6000.6131.0001.0001.0000.9990.9950.9710.8540.639
eco_net_debt_R$0.7490.6980.6990.6860.8290.6320.5990.6060.9990.9990.9991.0000.9970.9700.8560.636
eco_net_debt_R$_federal_govt0.7320.7110.6860.6900.8340.6320.6070.5920.9950.9950.9950.9971.0000.9680.8520.644
eco_total_revenue0.7130.6950.6560.6900.8260.6510.6160.5590.9710.9710.9710.9700.9681.0000.8460.596
exp_DIC_y0.5800.5550.4600.5640.7370.5220.5110.4220.8530.8530.8540.8560.8520.8461.0000.384
exp_trade_balance_y0.5310.2620.6730.2360.3680.2210.2350.5720.6390.6390.6390.6360.6440.5960.3841.000

Missing values

2024-02-06T10:06:35.221862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-06T10:06:35.559965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

dateeco_net_debt_R$eco_net_debt_R$_federal_govteco_GDP_R$_12_monthsCoffeeIron OreMeat indexSoybeansSugarbud_group_personal_spent_valuebud_type_mandatory_spent_valueexp_DIC_yexp_trade_balance_yCNYEURUSDeco_total_revenue
02001-01541333.74337611.991209046.140.07117622.18041165.93872948.36489157.6671716.267857e+101.363717e+1124.000.500.2385881.843701.971122633.832094
12001-02550253.48346552.501218911.041.06171022.18041167.67504646.16763854.2375856.267857e+101.363717e+1124.00-0.270.2475511.891532.045218150.773048
22001-03561959.20355037.501234635.040.68964622.18041174.21655445.31913151.0388216.267857e+101.363717e+1123.45-1.000.2616591.901652.161620083.742154
32001-04565464.63360694.521250830.740.28175422.18041174.33200243.72976447.7289436.267857e+101.363717e+1122.70-1.250.2644641.941642.184721889.774658
42001-05581727.09374879.991263306.042.49957922.18041177.67519045.17982452.1689866.267857e+101.363717e+1121.00-1.500.2856992.001342.360021186.038204
52001-06586060.21378378.891265570.038.96862622.18041179.21201746.93382750.9820746.267857e+101.363717e+1120.00-1.320.2790391.963602.304921530.527644
62001-07606367.82395250.931272918.635.97495722.18041179.88395551.46130849.9452056.267857e+101.363717e+1118.00-1.000.2943302.132832.431322237.149806
72001-08622953.86408431.081280805.436.21295822.18041180.99869650.38484446.9134066.267857e+101.363717e+1118.80-0.800.3089092.330612.551722229.496283
82001-09635685.65418457.601289198.635.56335822.18041179.29576547.51638443.2539546.267857e+101.363717e+1118.000.000.3233922.435842.671320706.019172
92001-10646099.14418907.031298386.534.40419422.18041174.70832344.28699240.3749666.267857e+101.363717e+1118.601.200.3277262.441672.707123620.440076
dateeco_net_debt_R$eco_net_debt_R$_federal_govteco_GDP_R$_12_monthsCoffeeIron OreMeat indexSoybeansSugarbud_group_personal_spent_valuebud_type_mandatory_spent_valueexp_DIC_yexp_trade_balance_yCNYEURUSDeco_total_revenue
2682023-055922818.045006344.8410476176.7134.351395178.470757135.034451140.153167143.0155353.503768e+112.214626e+1280.0059.80000.71675.42875.0959176812.099466
2692023-065992871.685169640.1210526477.7126.577771193.102605135.688510145.102054137.7599773.529402e+112.238686e+1279.5063.75990.66465.26264.8192180475.242972
2702023-076067062.705256528.0210568766.9118.099551194.614222135.839551153.047631133.0681993.555037e+112.262745e+1280.0066.50000.66385.22514.7415201828.779738
2712023-086163293.135315415.0610618789.0113.737167187.156730131.569790140.650878134.6639753.580672e+112.286805e+1280.0073.00000.67815.33534.9219172784.955827
2722023-096211220.805375619.9710666257.2112.053873205.631656128.305785134.309984147.1682083.606306e+112.310864e+1280.0072.10000.68595.30005.0076174315.643631
2732023-106253007.525402090.7910728151.8112.275013202.765497127.173451130.113647149.4593333.631941e+112.334924e+1271.7575.15000.69125.34535.0575215601.822968
2742023-116333747.915473823.0810801619.3120.503662224.109233123.753638136.294350151.6590243.657575e+112.358983e+1262.8078.40000.69165.38564.9355179392.377009
2752023-12NaNNaN10867756.9128.363340234.862411121.392080132.748212124.3125733.683210e+112.383042e+1259.0081.30000.68155.35164.8413NaN
2762024-01NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN68.4278.45000.69115.38054.9535NaN
2772024-02NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.68795.34534.9471NaN